Abstract
The main purpose of this study is to develop a new hazard evaluation technique considering the current limitations, particularly for shallow landslides. For this purpose, the Buyukkoy catchment area, located in the East Black Sea Region in the east of Rize province and the south of Cayeli district, was selected as the study area. The investigations were executed in four different stages. These were (1) preparation of a temporal shallow landslide inventory of the study area, (2) assessment of conditioning factors in the catchment, (3) susceptibility analyses and (4) hazard evaluations and mapping. A total of 251 shallow landslides in the period of 1955-2007 were recognised using different data sources. A 'Sampling Circle' approach was proposed to define shallow landslide initiation in the mapping units in susceptibility evaluations. To accomplish the susceptibility analyses, the method of artificial neural networks was implemented. According to the performance analyses conducted using the training and testing datasets, the prediction and generalisation capacities of the models were found to be very high. To transform the susceptibility values into hazard rates, a new approach with a new equation was developed, taking into account the behaviour of the responsible triggering factor over time in the study area. In the proposed equation, the threshold value of the triggering factor and the recurrence interval are the independent variables. This unique property of the suggested equation allows the execution of more flexible and more dynamic hazard assessments. Finally, using the proposed technique, shallow landslide initiation hazard maps of the Buyukkoy catchment area for the return periods of 1, 2, 5, 10, 50 and 100 years were produced.
| Original language | English |
|---|---|
| Pages (from-to) | 459-483 |
| Number of pages | 25 |
| Journal | Landslides |
| Volume | 8 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Dec 2011 |
| Externally published | Yes |
Funding
This study was supported by the Hacettepe University Scientific Research Unit (Ankara, Turkey) with the project 07 01 602 006. The authors would like to thank associate professors, Dr. Sebnem Duzgun and Dr. M. Lutfi Suzen for their constructive comments on the manuscript and assistant professor, Dr. Serhat Dag for his support during the field studies.
| Funders | Funder number |
|---|---|
| Hacettepe Üniversitesi | 07 01 602 006 |
Keywords
- Artificial neural networks
- Buyukkoy catchment area (Cayeli, Rize, Turkey)
- Hazard
- Sampling circle
- Shallow landslide
- Susceptibility